|
DESERT
3.0
|
#include <mS2C_Evo_lowlev.h>
Public Member Functions | |
| MS2C_Evo_lowlev (std::string) | |
| virtual int | recvSyncClMsg (ClMessage *m) |
| virtual modem_state_t | check_modem () |
| ~MS2C_Evo_lowlev () | |
| Public Member Functions inherited from UWMPhy_modem | |
| UWMPhy_modem (std::string) | |
| ~UWMPhy_modem () | |
| virtual void | recv (Packet *) |
| virtual int | command (int, const char *const *) |
| int | getID () |
| double | getPeriod () |
| std::string | getPathToDevice () |
| uint | getQueueLength () |
| int | getDebug () |
| std::string | getLogFile () |
| log_level_t | getLogLevel () |
| unsigned long int | getEpoch () |
| void | updatePktRx (Packet *) |
Private Attributes | |
| CheckTimer | checkTmr |
| DropTimer | dropTmr |
| MdriverS2C_Evo_lowlev | mDriver |
| int | bitrate_i |
| int | SL |
| int | msg_bitlen |
Additional Inherited Members | |
| Protected Member Functions inherited from UWMPhy_modem | |
| void | setConnections (CheckTimer *, UWMdriver *, DropTimer *) |
| virtual void | start () |
| virtual void | stop () |
| virtual void | startTx (Packet *) |
| virtual void | endTx (Packet *) |
| virtual void | startRx (Packet *) |
| virtual void | endRx (Packet *) |
| Packet * | popTxBuff () |
| virtual double | getTxDuration (Packet *p) |
| virtual int | getModulationType (Packet *p) |
| Protected Attributes inherited from UWMPhy_modem | |
| CheckTimer * | pcheckTmr |
| UWMdriver * | pmDriver |
| DropTimer * | pDropTimer |
| int | ID |
| double | period |
| Packet * | modemTxBuff [_MTBL] |
| int | t |
| Packet * | PktRx |
| std::string | pToDevice |
| int | debug_ |
| std::ofstream | outLog |
| std::string | logFile |
| char * | log_suffix |
| int | loglevel_ |
| int | SetModemID |
| uint | queue_length |
Class that implements the interface between ns2/NS-Miracle and the S2C EvoLogics modem using low level firmware (it derives UWMPhy_modem).
NOTE: For tcl-user: set node($id) [new "UW/MPhy_modem/S2CLowLevel" "path to the device"] Example: in case of a modem to be connected via TCP/IP socket "path to the device" would be the application port opened via ssh, 9200
Definition at line 56 of file mS2C_Evo_lowlev.h.
| MS2C_Evo_lowlev::MS2C_Evo_lowlev | ( | std::string | pToDevice_ | ) |
Class constructor.
| pToDevice_ | the path to the device that must be connected with NS-Miracle (e.g., /dev/ttyUSB0 for a serial connection) |
Definition at line 62 of file mS2C_Evo_lowlev.cpp.
| MS2C_Evo_lowlev::~MS2C_Evo_lowlev | ( | ) |
Class destructor.
Definition at line 79 of file mS2C_Evo_lowlev.cpp.
|
virtual |
Check the state of the modem and update its status.
NOTE: Not implemented on this interface with lowlevel firmware of the modem
Implements UWMPhy_modem.
Definition at line 90 of file mS2C_Evo_lowlev.cpp.
|
virtual |
Receive Sync CrossLayer Messages.
If the current type of the message is not handled by this module, will be propagated to the father class
| m | pointer to the ClMessage just received |
Implements UWMPhy_modem.
Definition at line 84 of file mS2C_Evo_lowlev.cpp.
|
private |
Definition at line 67 of file mS2C_Evo_lowlev.h.
|
private |
Object which schedules the check-modem events.
Definition at line 59 of file mS2C_Evo_lowlev.h.
|
private |
Object which sets the modem queue to reset status.
Definition at line 61 of file mS2C_Evo_lowlev.h.
|
private |
Object which drives the modem operations.
Definition at line 64 of file mS2C_Evo_lowlev.h.
|
private |
Definition at line 69 of file mS2C_Evo_lowlev.h.
|
private |
Definition at line 68 of file mS2C_Evo_lowlev.h.


Anthem Lights is an American Christian group originating from Nashville, Tennessee.
The group has released one EP under their former name, Yellow Cavalier and six albums under their current name.
The group's debut album was released May 10, 2011 by Reunion Records.
They are the best when it comes to doing covers.
gospel songs rendition are the best! They have the best harmonies.
The best thing about them is that they can make any song in any version.
Vocalist and keyboardist for the Christian rock group Anthem Lights who performs alongside Alan Powell, Joseph Stamper, and Chad Graham.
Indiana-born singer known for singles like "Best Friends," "I Want That," and "Move in the Right Direction." He also had a role in the Trinity Broadcasting Network sitcom iShine Knect. He later joined the group Anthem Lights.
One fourth of the Christian rock group Anthem Lights who provides vocals and plays the piano, guitar, and drums.
Lead vocalist for the Christian rock group Anthem Lights that originated in Nashville, Tennessee. The group has released charting Top US Christian Albums and Top Heatseeker albums which includes a self-titled debut and their 2014 release You Have My Heart.


Browser: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_3) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.56 Safari/535.11
My BBLog Addon Version is: 1.2
I noticed that both variants of the G-18 (both suppressed and regular) are missing from the PIST sorted section under the weapons tab.


1 instantiation of RemoteCommandInfo
System.Management.Automation (1)
engine\InvocationInfo.cs (1)
488
commandInfo = new
RemoteCommandInfo
(name, type);
4 references to RemoteCommandInfo
System.Management.Automation (4)
engine\InvocationInfo.cs (4)
118
MyCommand =
RemoteCommandInfo
.FromPSObjectForRemoting(psObject);
450
RemoteCommandInfo
.ToPSObjectForRemoting(this.MyCommand, psObject);
477
internal static
RemoteCommandInfo
FromPSObjectForRemoting(PSObject psObject)
479
RemoteCommandInfo
commandInfo = null;


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I completed my PhD at the University of Pisa in 2013, under the supervision of Prof. Ugo Montanari. In 2015 I moved to Radboud University for a postdoc, and in 2016 I joined the Programming Principles, Logic and Verification group at University College London. In 2020 I joined Royal Holloway, University of London as Lecturer.
My research focuses on formal methods for various classes of real-world systems, including concurrent/distributed and infinite-state systems. In recent years, I became interested in the use of artificial intelligence in automated verification. My long-term research plan consists in the use of artificial intelligence to solve challenging real-world modelling and verification problems.


Landscape Dynamics
- Water, wind, gravitation, plants, animals and humans continuously change landscape surface
- Land surface processes are poorly understood and our predictive capabilities are limited
- Hazard management decisions are often based on insufficient information
Advances in 3D mapping
- Lidar and sUAS: increased capacity to
map 3D landscapes and assess the change on (almost) continuous basis
- Development of tools for terrain time series analysis
- Data for predictive models development, calibration and validation
to improve hazards management
Multivariate terrain time series analysis
- 1D lines - transects, horizontal migration of line features: shorelines, dune crests, channels
- 2D rasters - per cell statistics, core and envelope, rate of vertical change
- 3D rasters - space-time cube contour evolution isosurfaces
Hardin, E., Mitasova, H., Tateosian, L., Overton, M., 2014, GIS-based Analysis of Coastal Lidar Time-Series, Springer Briefs in Computer Science, Springer, New York, 84 p.
Analysis of migrating landforms
Horizontal migration is often associated with change in landform geometry
which is hard to measure with standard transects
Sand dune on NC coast: Jockey's Ridge 1974 - 2012
Transect based analysis
Jockey's Ridge dune
horizontal migration: extracting crests and manually measuring distances along transect,
to derive spatially agreggated rates
Similar famous coastal dunes: Slowinski National Park, Poland, Pyla in France, Totori in Japan
Gradient field of horizontal migration
- Measure the rate and direction of landform migration at any point in space and time
- Study spatial pattern of migration vectors
We use the concept of Space-Time Cube where for any contour z=const we can define a function
t=g(x,y)
where t is the time when this contour is located at the point (x,y).
For g(x,y) to be a function, we need to segment time series of contours into
non-intersecting segments.
Then rate of migration is inverse of the gradient magnitude of the function g(x,y).


A bitmap that destroys the underlying reference when destructed.
More...
#include <pebble_graphics_types.hpp>
A bitmap that destroys the underlying reference when destructed.
◆ CreateBlank() [1/2]
Creates a new blank GBitmap on the heap initialized to zeroes. In the case that the format indicates a palettized bitmap, a palette of appropriate size will also be allocated on the heap.
- Parameters
-
| size | The Pebble image dimensions as a GSize. |
| format | The GBitmapFormat the created image should be in. |
- Returns
- The GBitmap.
◆ CreateBlank() [2/2]
Creates a new blank GBitmap on the heap, initialized to zeroes, and assigns it the given palette. No deep-copying of the palette occurs, so the caller is responsible for making sure the palette remains available when using the resulting bitmap. Management of that memory can be handed off to the system with the free_on_destroy argument.
- Parameters
-
| size | The Pebble image dimensions as a GSize. |
| format | the GBitmapFormat the created image and palette should be in. |
| palette | a pointer to a palette that is to be used for this GBitmap. The palette should be large enough to hold enough colors for the specified format. For example, GBitmapFormat2BitPalette should have 4 colors, since 2^2 = 4. |
| free_on_destroy | Set whether the palette data should be freed along with the bitmap data when the GBitmap is destroyed. |
- Returns
- The GBitmap.
◆ CreateFromPngData()
| static GOwnedBitmap Pebble::GOwnedBitmap::CreateFromPngData |
( |
const uint8_t * |
png_data, |
|
|
size_t |
png_data_size |
|
) |
| |
|
inlinestatic |
Create a GBitmap based on raw PNG data. The resulting GBitmap must be destroyed using gbitmap_destroy(). The developer is responsible for freeing png_data following this call.
- Note
- PNG decoding currently supports 1,2,4 and 8 bit palettized and grayscale images.
- Parameters
-
| png_data | PNG image data. |
| png_data_size | PNG image size in bytes. |
- Returns
- Bitmap
◆ CreatePalettizedFrom1bit()
| static GOwnedBitmap Pebble::GOwnedBitmap::CreatePalettizedFrom1bit |
( |
const ::GBitmap * |
src_bitmap | ) |
|
|
inlinestatic |
Given a 1-bit GBitmap, create a new bitmap of format GBitmapFormat1BitPalette. The new data buffer is allocated on the heap, and a 2-color palette is allocated as well.
- Parameters
-
| src_bitmap | A GBitmap of format GBitmapFormat1Bit which is to be copied into a newly created GBitmap of format GBitmapFormat1BitPalettized. |
- Returns
- The newly created 1-bit palettized GBitmap.
- Note
- The new bitmap does not depend on any data from src_bitmap, so src_bitmap can be freed without worry.
◆ CreateSubBitmap()
Create a new GBitmap on the heap as a sub-bitmap of a 'base' GBitmap, using a GRect to indicate what portion of the base to use. The sub-bitmap will just reference the image data and palette of the base bitmap. No deep-copying occurs as a result of calling this function, thus the caller is responsible for making sure the base bitmap and palette will remain available when using the sub-bitmap. Note that you should not destroy the parent bitmap until the sub_bitmap has been destroyed.
- Parameters
-
| [in] | base_bitmap | The bitmap that the sub-bitmap of which the image data will be used by the sub-bitmap |
| sub_rect | The rectangle within the image data of the base bitmap. The bounds of the base bitmap will be used to clip sub_rect . |
- Returns
- A GBitmap.
◆ CreateWithData()
| static GOwnedBitmap Pebble::GOwnedBitmap::CreateWithData |
( |
const uint8_t * |
data | ) |
|
|
inlinestatic |
Creates a new GBitmap on the heap initialized with the provided Pebble image data.
When the GBitmap is destroyed but the image data will not be freed automatically. The developer is responsible for keeping the image data in memory as long as the bitmap is used and releasing it after the bitmap is destroyed.
- Note
- One way to generate Pebble image data is to use bitmapgen.py in the Pebble SDK to generate a .pbi file.
- Parameters
-
| data | The Pebble image data. Must not be NULL. The function assumes the data to be correct; there are no sanity checks performed on the data. The data will not be copied and the pointer must remain valid for the lifetime of this GBitmap. |
- Returns
- The GBitmap.
◆ CreateWithResource()
| static GOwnedBitmap Pebble::GOwnedBitmap::CreateWithResource |
( |
uint32_t |
resource_id | ) |
|
|
inlinestatic |
Creates a new GBitmap on the heap using a Pebble image file stored as a resource.
- Parameters
-
| resource_id | The ID of the bitmap resource to load |
- Returns
- The GBitmap.
◆ SetBounds()
| void Pebble::GOwnedBitmap::SetBounds |
( |
const GRect & |
bounds | ) |
|
|
inline |
Set the bounds of the given GBitmap.
- Parameters
-
| bounds | The bounding box to set. |
- See also
- GetBounds
◆ SetData()
| void Pebble::GOwnedBitmap::SetData |
( |
uint8_t * |
data, |
|
|
GBitmapFormat |
format, |
|
|
uint16_t |
row_size_bytes, |
|
|
bool |
free_on_destroy |
|
) |
| |
|
inline |
Set the bitmap data for the given GBitmap.
- Parameters
-
| data | A pointer to the bitmap data |
| format | the format of the bitmap data. If this is a palettized format, make sure that there is an accompanying call to gbitmap_set_palette. |
| row_size_bytes | How many bytes a single row takes. For example, bitmap data of format GBitmapFormat1Bit must have a row size as a multiple of 4 bytes. |
| free_on_destroy | Set whether the data should be freed when the GBitmap is destroyed. |
◆ SetPalette()
| void Pebble::GOwnedBitmap::SetPalette |
( |
GColor * |
palette, |
|
|
bool |
free_on_destroy |
|
) |
| |
|
inline |
Set the palette for the given GBitmap.
- Parameters
-
| palette | The palette to be used. Make sure that the palette is large enough for the bitmap's format. |
| free_on_destroy | Set whether the palette data should be freed when the GBitmap is destroyed or when another palette is set. |
- See also
- GetFormat
The documentation for this class was generated from the following file:


Support for scatter plot analysis, including condition-condition analysis (formerly called bait-bait) and readout specificity has been added to ProHits-viz 2.0.
These tools come with an updated interactive viewer that allows for customization of the colour and size of points, addition of fold-change lines, toggling between linear and log axes, and selection of points for analysis, such as GO enrichment. You can try out the new features with an example dataset.
If you would like to provide feedback, report bugs or request new features, please use GitHub. If your issue has already been created on GitHub by someone else, you do not need to do anything unless you would like to provide additional comments or context.
Using GitHub


To create new tests, enter some ASCIIMath below and hit Math Test. Copy that into unittests.js, then run it to ensure it renders as expected.
Create new test: Make Test


3:47 am
Just took approximately 25 mg of IAP.
4:12 am
Getting pretty nice head euphoria. Not really speedy, but euphoric. Nose
feels tingly. Music euphoria. It is a possible SSRI so I do not know how it is going to affect my DXM trips, but I like what I am seeing thus far. Seems to increase the flow of conversation, but I do not notice any actual mood elevation that comes accustomed to GHB.
4:23 am
Nose tingling has gone away, now it is just head euphoria, music euphoria,
and an opening of senses without any visual distortion. I feel good. It does feel a bit like MDMA.
4:58 am
Euphoria comes in waves, head is tingly and feels good. Like a low dose of
MDMA - will experiment with 50 mg next time. I feel open and talkative.
5:21 am
Consumed some alcohol. I feel very open and empathetic. Feeling really good. Not intense but very nice positive attitude and energy. Not 'let's go out and do something' energy like with GHB but just happy-go-lucky, content with life, talking a bit more than normal, easy to converse, etc. Alcohol definitely adds to the euphoria.
6:29 am
Euphoria, disinhibition (probably from the alcohol added - very synergistic). I could only imagine what GHB and IAP would do together. The euphoria is amazing - it encompasses my entire head. And I feel more open and talkative. If only this could be marketed as an antidepressant instead of Paxil and Prozac, the world would be a MUCH happier place.
6:50 am
Just got stoned with AK-47 strain.  I have found the second best thing to GHB. Perhaps even better, if tolerance is not an issue and I see how it goes at higher doses.
THC provides an amazing synergy with this drug. I now have a full-body buzz
going. The two go hand in hand. I imagine that GHB+IAP+THC would bring about a full-body orgasm, much like MDMA+N2O.
Sleep was difficult. I was not able to sleep until 12 hours after ingesting
it. It is a lot longer acting than I thought - the alcohol and marijuana may have prolonged the effects, but I doubt it. Sleep, when it came, was normal. Today I felt fine, and I notice there is no interaction with DXM. Appetite is normal, everything else fine. Since it lasts so long, I probably wouldn't do this more than once a week.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Unorganized helper functions for Apos.Input. Also holds some important variables for this library to work correctly.
Pass your game class here.
Checks if the game is active. Usually this is when the game window has focus.
The game window.
The game window's width.
The game window's height.
The mouse's previous state.
The mouse's current state.
The keyboard's previous state.
The keyboard's current state.
An array with all gamepads' previous states.
An array with all gamepads' current states.
An array with all gamepads' info.
An array with all gamepads' deadzone settings.
A touch collection that holds the previous and current touch locations.
Gives info about a touch panel.
Useful for handling text inputs from any keyboard layouts. This is useful when coding textboxes.
Maps a MouseButton to a function that can extract a specific ButtonState from a MouseState.
Maps a GamePadButton to a function that can extract a specific ButtonState from a GamePadState.
| public static void Setup( |
| ) |
Call Setup in the game's LoadContent.
| game Game | Your game object. |
Call this at the beginning of your update loop.
Call this at the end of your update loop.
This class is designed to help with receiving text input events through reflection magic.
| public static void processTextInput( |
| ) |
This function receives TextInput events from the game window.
| sender object | This gets ignored. |
| e T | Contains a character and a key. |
The mouse's previous state.
The mouse's current state.
The keyboard's previous state.
The keyboard's current state.
An array with all gamepads' previous states.
An array with all gamepads' current states.
An array with all gamepads' info.
An array with all gamepads' deadzone settings.
A touch collection that holds the previous and current touch locations.
Gives info about a touch panel.
Useful for handling text inputs from any keyboard layouts. This is useful when coding textboxes.


Fashion is the Armor to survive the reality of everyday life
The joy of dressing is a Art.
Beauty begins the movement you decide to be yoursellf.
style is hard to define but easy to spot.
Fashion is a trend.Style lives within a person .
Style is always in fashion.
The beauty of the soul is wrapped in modest fashion.
Fashion is about dreaming and making other people dream.


DNS validity reminder
Your DNS for the domain #DOMAIN_NAME# is going to expire in #EXPIRY#.
Sincerely HON IT-team


PROJECTS
{{ project.name }}
Mentored by
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ABOUT PROJECT
{{ project.about }}
Go To Home Page


MuniHac 2016 2nd of September
David Luposchainsky
quchen /ˈkuːχn̩/
TNG Technology Consulting
main = putStrLn "Hello world"
$ ghc helloworld.hs
[1 of 1] Compiling Main ( helloworld.hs, helloworld.o )
Linking helloworld …
$ ./helloworld
Hello world
isEmpty [] = True
isEmpty (x:rest) = False
swapTop (x:y:rest) = y:x:rest
swapTop rest = rest
elem x [] = False
elem x (y:ys) = x == y || elem x ys
-- take 3 [1..] ==> [1,2,3]
take n [] = ...
take ...
take 0 xs = []
take n [] = []
take n (x:xs) = x : take (n-1) xs
ProjectEuler #1: Find the sum of all the multiples of 3 or 5 below 1000.
sum [1,2,3] = 6
filter (\x -> even x) [1,2,3] = [2]
True || False = True
solution = sum (filter (\x -> mod x 3 == 0 || mod x 5 == 0) [1..999])
x :: (Integer, Integer) -> (Integer, Integer)
y :: (a, a) -> (a, a)
z :: (a, b) -> (b, a)
x :: (a -> Bool) -> [a] -> Integer
y :: [a] -> [b] -> [(a,b)]
z :: Integer -> [a] -> [a]
w :: (a -> b) -> [a] -> [b]
f :: [a] -> (a -> Bool) -> [a]
whileTake [] p = []
whileTake (x:xs) p = if p x then x : whileTake xs p
else []
whileDrop [] p = []
whileDrop (x:xs) p = if p x then []
else x : whileDrop xs p
whileDrop xs p = []
:set -Wall
in GHCi
unLeft (Left l) = l
unCrazy (Left ([], Just b, Right _)) = b
unCrazy (Right r) = r


>>
<<
Usr
Pri
JfC
LJ
Phr
Dic
Voc
!:
Help
Primer
Comment
The primitive
NB.
starts a comment that runs to the end of the line.
2 + 23 NB. this is a comment and is not executed 25
>>
<<
Usr
Pri
JfC
LJ
Phr
Dic
Voc
!:
Help
Primer


About
Photo Masher!
Ugly mashups of your face
Photobooth
Mashups
Oops! You don't have enough pictures yet. Switch back to the photobooth mode and take at least 3
Photobooth
Thing
Mashup
Click 1
Click 3
Click 8
MASHUP!
Go Crazy
Woah.. settle down there.


--- authors: - shelby-hassell #categories: ["one", "two"] contact: "s.sleete [at] gmail.com" #date: 2020-03-03 02:02:02 from: "Troy, Michigan, USA" #hero_classes: "background-color--main-dark color--white" job_title: "Bride" meta: description: "Mother, empath, reality TV junkie" image: alt: "Shelby Sleete" src: 1200x630/shelby-sleete.jpg # robots: "index,follow" # title: "Overrides the tab title and social titles" #permalink: /people/shelby-hassell/ #published: true #sitemap: true short_name: shelby #tags: ["three", "four"] title: "Shelby Hassell" twitter: creator: "@all_maudra" weight: 1 --- {% include atoms/heading.html children="About Shelby" classes="text-shadow--second-light--solid" level="2" %}
Shelby is sunshine considerately wearing black so as not to blind you.
She grew up in Troy, Michigan, youngest of three. After a brief stint in Virginia, she returned with her family to Michigan, and lived that community college and retail life. She met her first husband, moved to Flint, MI, and gave birth to perfect Ellie, to whom she is the ideal mother.
Shelby describes herself as 'indoorsy', and loves reality TV like the various Real Housewives franchises. She's also super into podcasts, fat babies, Chili's and other chain restaurants, and alternately sipping Barefoot Riesling and getting backrubs from Brad.


Generate !
As PNG
By Columns
Bar
Horiz. Bar
Area
Line
Point
Pie
Donut
Horiz. Area
Fruit
Sugar
Fiber
Apple
5
1
Kiwi
2
4
Banana
1
7
Series
Stacked:
No
100%
Yes
Side All
Self
Marks
Tool tips
Smooth
Pointer
Legend
Legend:
No
Left
Right
Top
Bottom
Chart
WebGL 3D
This browser does not seem to support HTML5 Canvas.
Size:


Template Intro
Elective Template
Elective resources are content that is not part of the core in-person curriculum roadmap, but that is a valuable framework, tool, or technique to learn as a web application engineer.
It is shorter than a normal topic outline and typically uses more external resources.
Copy this file as a starting point and create your new file in the /electives
folder. Your elective should include the following sections:
Write one short paragraph or bulleted list than answers the following:
What is the technology? Instead of using the project’s own description, how would you describe it to a junior engineer?
Link to a free hands-on tutorial on getting started with the app. Example for React from their official docs.
Please only include one tutorial here, one you gave gone through yourself and vetted for quality. If you have a few you like, choose the best one for a beginner audience. Any runner-ups can go in the “Further Reading” section below.


Airharbor Guard:
From here you can take an airship to Gracia. Our mission is to maintain the public peace, as ordered by the Magister and Master!
You should know that there are rumors of possible terrorist attacks on the airships, as well as reports of spies trying to infiltrate our realm here!
Quest.


@Retention(value=RUNTIME)
public @interface HGIgnore
This annotation can be used to mark a bean property to be ignored when a HyperGraph type is being automatically created for it.
In general, a bean property is included as a record slot in HyperGraph whenever
both a setter and a getter are present for it. You can annotate either the setter,
getter, or the member field declaration with @HGIgnore
in order to
indicate that the property it NOT to be recorded in HyperGraph.
The most common case where a bean property is to be ignored is when a
getter/setter combination does not actually correspond to some independent
internal bean field. Note that in case it DOES correspond to an
internal bean field, but you don't want to save it anyway, you can also
declare the field as transient
- the standard Java
mechanism to ignore a field during serialization/persistence.


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Nepal
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Current Address
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Contact No.
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:
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Mobile
Home
Work


Introducing Shop
Since I've starting working at BBS Concept I've spend a lot of time working on PrestaShop. And when you spend a lot of time with something you end up trying to automate as much as possible to be more efficient.
About a year ago, I wanted to work on a CLI to make my development workflow faster. Back then it ran on Node.js (I've just discovered it and was excited to be able to do server side stuff with JavaScript instead of PHP because... well because PHP).
A few weeks after, Google revealed Yeoman. I got an early access to it and found it pretty good, particularly the generators. So I decided to re-write my tool (that wasn't even finished) to make it a Yeoman generator. But it is very opinionated and blocked me in some actions I wanted to do. Plus, Yeoman was built mainly for web apps and use Grunt all over the place. I don't use Grunt when working on PrestaShop projects, and a bunch of built in stuff does not fit: the build processes for examples are useless in that case. So I dropped the project.
After that, I started using a bunch of stuff to substitute the lack of CLI. I wrote Makefiles and Rakefiles all over the place to have tasks ready to make some processes faster. After months of tweaking those tasks, I know have a pretty good workflow that I find great.
A few month ago I started learning Ruby and was amazed how easy it was to write gems (and how Ruby felt easy and obvious). So I thought I could give it a try once again and turn those Rakefiles into a proper CLI.
PrestaShop CLI?
Since PrestaShop 1.5.4 a CLI exists... to install the framework. Well that's good but that's not a lot (compared to Rails or Symfony for example). Plus the CLI is not very appealing: You have to use one of those syntax
$ php install_cli.php --domain=domain.com --db_name=prestashop --toomany=arguments
That's the kind of thing I can't remember and I would have to look at the manual everytime, so it's not really faster. Plus you don't install new projects as often as you create files.
No offense to the PrestaShop developers, but... come on. You can do better.
Introducing Shop
So my goal with Shop was to make a CLI as easy as possible, unopinionated and that use as little configuration as possible.
Basically Shop helps you create a new PrestaShop project, then create files and modules, and perform simple task like cleaning the cache.
The advantage is that it lets a lot of freedom in that it creates the bare minimum everytime. The idea is not to have a complete set of generator but just to type less. Instead of
$ mkdir -p modules/mymodule && cd modules/mymodule
$ touch mymodule.php
$ mkdir -p views/templates/hook
You can just run
$ shop module mymodule
If you work a lot on PrestaShop and feel like it could be useful, feel free to try it. You can install it with
$ gem install shop
You can then see the available commands by running
$ shop help
The source and the whole documentation are available on GitHub. If you have any issues or feedback feel free to ping me on twitter or to open an issue on GitHub.


"Dad do you like smooth jazz?" "Um no, not really." "So, do you like bumpy jazz then?"
7/19/2013, 5:57:14 PM
Favs: 0
Retweets: 0
link--- layout: tweet ---
"Dad do you like smooth jazz?" "Um no, not really." "So, do you like bumpy jazz then?"
7/19/2013, 5:57:14 PM
Favs: 0
Retweets: 0
link


{include file="public:head" /}
{include file="public:nav" /}
加载更多
{include file="public:rightslider" /}
{include file="public:footer" /} {include file="public:scripts" /}


Internal platform development
Clarity goes side by side with simplicity. We have built smart software tools to empower businesses. Making company processes more effective and user friendly boosts their growth and this gives us great satisfaction of well done job.
Case study
Let's have a look at Eloquia. This private school internal system made staff management, accountancy, student communication and their progress assesment much easier. The system provides different levels of access and rights for each user type.
Aviation Language Assessment Tool, enabling smooth language proficiency level assessment, was one of the components of the platform.
Our role
To design the product's architecture we analyzed the internal company processes, their needs, main groups of users, their problems and the goals each part wanted to achieve.
We have implemented the solution from scratch, tested it, and developed additional functionalities needed.
The result for the company was no more repetitive work, faster access to information, easier progress assessment, and absolute clarity of what's going on in the company.
The outcome
He created a well working application which included all the functionality we asked for. His communication and his availability are outstanding and he regularly brings in his ideas and suggestions, which were all very much appreciated.
Technology and approach
The base of the good cooperation in this kind of system is comprehensive specification that puts both the client and developers on the same page before we start working on the sytem. In order to keep the assessment tool safe and secure, we implemented the logic that prevented users from abusing the application. We achieved this by effective communication between the browser and the central server of the platform.
We've used Domain Driven Design approach and with close cooperation with the client defined the domain details and designed the architecture. This helped especially when we needed to develop the booking system, which was planned to be flexible and configurable.
Other system, similar opinion
His capacity to consider the ins and outs of a project make him a trusted partner to me and my clients. Well organized, providing reliable estimates and robust code.


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Level of confidence that token is an accurately-transcribed word
extremely low
very low
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undetermined
medium
high
very high


Along Cayuga Street, you can visit 60 different handcrafting artisans. Items that will be sold include jewelry, ceramics, home decor, body care, specialty gifts, unique sculpture, clothing, and much more. Many of these items are things that young children may need or want, and many of the items sold are suited for adults as well. Shopping at AppleFest provides parents with another source of entertainment, enhancing the overall experience for the family.


|
BTK
0.3dev.0
Open-source library to visualize/process biomechanical data
|
Compute forces and moments from the wrench and quaternion inverse dynamic method. More...
Public Types | |
| typedef SharedPtr< const WrenchQuaternionInverseDynamicMethod > | ConstPointer |
| typedef SharedPtr < WrenchQuaternionInverseDynamicMethod > | Pointer |
| Public Types inherited from btk::InverseDynamicMethod | |
| typedef SharedPtr< const InverseDynamicMethod > | ConstPointer |
| typedef SharedPtr < InverseDynamicMethod > | Pointer |
Public Member Functions | |
| virtual BTK_MODELS_EXPORT void | Run (std::list< SegmentCollection::Pointer > chains, const double gravity[3], double sampleFrequency, double scaleToMeter) const |
Static Public Member Functions | |
| static Pointer | New () |
Protected Member Functions | |
| WrenchQuaternionInverseDynamicMethod () | |
| Protected Member Functions inherited from btk::InverseDynamicMethod | |
| InverseDynamicMethod () | |
Compute forces and moments from the wrench and quaternion inverse dynamic method.
Assumptions:
Note: This implementation does not require to set the groud reaction force to the endpoint (distal joint wrench) of the segment. Instead of this, you can set it as an external wrench (for example the Segment::ForcePlatformWrench or a Segment::UserWrench). With this implementation it gives the possibility to have several segments with external wrenches (other than the extremities). An exemple could be the use of a multi-segment foot model (Oxford foot model?) where the groud reaction wrenche are splitted into the segments.
Smart pointer associated with a const WrenchQuaternionInverseDynamicMethod object.
Smart pointer associated with a WrenchQuaternionInverseDynamicMethod object.
|
protected |
Constructor
|
virtual |
Method containing the algorithm to compute dynamic inverse.
The values of the array gravity must be set in meter by second squared (m/s²).
The parameter scaleToMeter is a scale factor used to compute the forces and the moment depending of the linear acceleration and the tensor of inertia. To be homogeneous in the units, the computation must use a mass set in kilogram (kg), an acceleration set in meter by second squared (m/s²) and a tensor of inertia in kg·m². So, if the unit of the trajectories are the millimeter, then the variable scaleToMeter must be set to 0.001 to scale correctly the internal variables.
Reimplemented from btk::InverseDynamicMethod.


Employee Manager (For IT) is an app for the IT professional. It isn't just a contact manager, it is an information manager. Add/Edit/View/Remove employees to keep an updated list of current employees.
Employee Manager (For IT) is an app for the IT professional. It isn't just a contact manager, it is an information manager. Add/Edit/View/Remove employees to keep an updated list of current employees.


I caught fire coding. I started coding in school time where python is just treated, like a subject but so long I took this as a language of communication with computer. I mean seriously, how could it not be great when it's named after Monty python's flying circus? I took a great interest in coding with python as developing a piece of basic knowledge about python. Later when I explored much more about python I came to know about it is more than a basic programming language.
I developed my first project in python yeah it's true. My specialties include quickly learning new skills and programming languages, problem-solving, responsive design principles, and the Model View View Model(MVVM) and model view controller(MVC) methods of organizing code. So far I have python , Javascript, C#, jQuery, Bootstrap, PHP, Database, and Git/Github under my belt. I've started learning Node.js, MongoDB, Express.Js, AI and ML along with associative projects. I'm still enthusiastically grabbing onto any other programming languages, frameworks, or principles I can integrate into the coding web in my head.
View ResumeI developed a Quiz app based on PHP & MySQL which is used in an Event conducted by CSI student chapter i.e WISEENAIRE. Basically this web app which consists of set of a question with a limited time of 30 sec each if unable to attempt within the time the question is automatically changed with a new question. And a number of 120+ participated in this round.
This was my first team project where a group of 6 members developed our college app which is currently active with 2000+ users and also available in play store. I acted as a backend developer in the development process.
Currently, I am working on a python-based routine app that analyzes the routine of the user. Based on the activities also estimates the daily right habits for the user.
[(+91)<PHONE_NUMBER>]
Call to Action


In this blog entry, I will be talking about my experience in learning core aspects during my studies at Dev Academy so far.
One thing I learned is that, before Dev Academy, I wasted too much time due to overvaluing technical knowledge over planning. Recently, I've been learning about how to write code following a process. Before that however I would always just jump straight into writing the solution and perfecting it all in go with no prior planning, because of confidence about my technical knowledge. However this was why I made many mistakes and why I take too much time to write solutions.
Another thing I learned was that I'm too harsh on myself at times. I always felt dumb and unaccomplished whenever I wasn't managing to make things work no matter how much time and effort I put in, especially when I was at university. But having learned about things like the growth mindset and neuroplasticity, I feel that I truly am capable of growth and improvement as a person, but I just need to change the way I approach things, such as taking things one step at a time rather than shoot for the end goal.
One last thing I learned was that time management is my pitfall. At university, they'd always just hand us large assignments without suggestion on how we should manage our times. If Dev Academy did not teach me things like timeboxing, I feel I would not be meeting the deadlines I have met so far.
In learning and programming, it is easy to lose control of workload, time, relationships, and your own health. But values, empathy and self-awareness can prevent this. Values give you direction in terms of making important, independent decisions and setting standards for yourself and the people around you. Empathy sustains healthy relationships with the people around you, creating great learning progress and a stable working environment. Self-awareness means you keep in check with yourself, what you're doing right or doing wrong, so that you do not end up burning out and getting frustrated.
Seeing results from all the core skills suggested. People have always told me to manage my work and time better, but I did not see any success because I did not know how to do that. When I finally learned how to in core skills, I saw how much easier and more manageable things became, which really surprised me.
Exploring my own values, which were overall honesty, patience and empathy. I am quite an anxious person and find it hard at times to find a solid grounding in my own values.
Having gone through hell at university, I see exactly why Dev Academy focuses so much on core learning.  This is especially true with programming as it can get incredibly difficult and tiresome. Dev Academy teaches core skills so that people know how to manage their work and time, which means that even the less experienced or intelligent people are able to catch up and not burn out. The old me would think that practicing programming repeatedly would make me a successful programmer, and that these core skills are a waste of time. But that is not true at all. At the end of the day, core skills should always be prioritised. I am an incredibly motivated and hard-working person, and feel that if I stay true to that, and apply the core skills that I have learned, I will be successful.


Borrowing periods & overdue charges for physical titles
Charges and fines have been frozen during the coronavirus situation and all new loans will be extended to four weeks. CD and DVD charges have been suspended.
Find out more about the library service during coronavirus →
We don’t charge a fee for reserving a title unless we need to get it from another library service.
| What you can borrow | Loan period | Maximum titles | Charge | Overdue charge | Maximum overdue charge (per title) |
|---|---|---|---|---|---|
| Adult books (each) | 3 weeks | 20 | Free | 15p per day | £7.35 |
| Young Adult books (each) | 3 weeks | 20 | Free | 1p per day | 49p |
| Children’s books (each) | 3 weeks | 20 | Free | No charge | N/A |
| DVDs Premier | 1 week | 6 | £3.00 | 66p per day | £32.34 |
| DVDs (1-2 discs) | 1 week | 6 | £1.00 | 33p per day | £16.17 |
| DVD sets (3+ discs) | 2 weeks | 6 | £2.00 | 66p per day | £32.34 |
| CDs (music) | 1 week | 6 | £1.25 | 13p per day | £6.37 |
| Spoken Word (1-2 cass/CD) | 3 weeks | 6 | Free | 13p per day | £6.37 |
| Spoken Word (3+ cass/CD) | 3 weeks | 6 | Free | 25p per day | £12.25 |
|
Games (PlayStation2, DS and Wii) |
1 week | 2 | £3.00 | 78p per day | £38.22 |
| Language courses, Single and double items | 6 weeks | 6 | Free | 26p per day | £12.74 |
| Language courses, Sets (3+) | 6 weeks | 6 | Free | 37p per day | £18.13 |
| CDROMs – leisure | 1 week | 6 | £1.55 | 28p per day | £13.72 |
| CDROMs – education | 1 week | 6 | Free | 28p per day | £13.72 |
|
Audio described films on CD: includes the soundtrack, music, sound effects and the dialogue along with the audio description. Reserve here |
1 week | 6 | Free | 13p a day | £6.37 |
| Audio described films on DVD: DVDs with audio description (RNIB). More information can found at: RNIB | 1 week | 6 | Free | 33p a day | £16.17 |
If items have not been returned, overdue notices are sent after 21 and 42 days, and a bill for replacement is sent after 72 days. The bill includes the overdue charge as well as the replacement cost.


dep is a package manager
Dep has a four state system: 1. Current project source code 2. Manifest 3. Lock (contains the dependency graph) 4. Source code of the dependency itself
Add introduces new dependencies into the depgraph - which means a new gopkg lock is generated w new dependency - or a version constraint is added
Update (confusing)


{% extends "base.html" %} {% block title %}Sorry, your details do not match what we have in our records{% endblock %} {% block content %}
This may be because your GP practice does not have the correct details for you.
You may be able to make your choice online by giving us your NHS number.
Use my NHS number {% include 'snippet/other-ways-to-manage-your-choice.html' %}


rt-repeat
Repeats a node with its subtree for each item in an array. This is implemented by creating a method that is passed to a map call as a callback. It creates a real context for the iterated variable.
The syntax is rt-repeat=\"itemVar in arrayExpr\". Within the scope of the element, itemVar will be available in javascript context, and also an itemVarIndex will be created to represent the index of the item.
If the definition is myNum in this.getMyNumbers(), than there will be 2 variables in the scope: myNum and myNumIndex. This naming is used to allow nesting of repeat expression with access to all levels.
Online documents
Sample:

{myNumIndex}. {myNum}

Compiled:
define([
'react',
'lodash'
], function (React, _) {
'use strict';
function repeatMyNum1(myNum, myNumIndex) {
return React.DOM.div({}, myNumIndex + '. ' + myNum);
}
return function () {
return _.map(this.getMyNumbers(), repeatMyNum1.bind(this));
};
});


Features
- Lists available armor sets in game
- Displays materials required to complete armor upgrades
- Click set name to collapse/expand
- Checkbox to include/exclude individual sets from item count
- Right click or long hold to select/deselect all checkboxes
- Click each item to indicate if you have completed that upgrade (tick)
- Right click any item to tick/untick whole set
- Count updates based on what sets you are tracking and what items you have already upgraded
- Items and quantities to finish upgrading your sets indicated at the bottom of the page
- Hover over an item in this list to see what sets it is needed to finish
- Click an item in this list to see the items wiki entry
- Set completion/inclusion saved between visits using local storage.


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| JavaScript | More JS | Functional + Quality | Paradigms | Data Structures |
|---|---|---|---|---|
| $100 | $100 | $100 | $100 | $100 |
| $200 | $200 | $200 | $200 | $200 |
| $300 | $300 | $300 | $300 | $300 |
| $400 | $400 | $400 | $400 | $400 |
| $500 | $500 | $500 | $500 | $500 |
$100
What is a constructor (method)?
$200
What are parameters?
$300
What is prototypical inheritance?
$400
What is an object literal?
$500
What is apply? (Function.prototype.apply())?
$100
Hoisting
$200
What is scope?
$300
(Unexpected) Shadowing
$400
Lexical scoping
$500
What is a closure?
$100
An error that occurs when a language’s rules are violated, such as a misspelled keyword or incorrect punctuation.
What is a Syntax Error?
$200
A function that addition to returning a value, modifies some state or has an observable interaction with calling functions or the outside world has a …
What is a Side Effect?
$300
What is cohesion?
$400
What is Coupling?
$500
What is Arity?
$100
What is Object-Oriented (programming) ?
$200
Building the structure and elements of programs, treats computation as the evaluation of mathematical functions and avoids changing-state and mutable data
What is functional (programming)?
$300
Programming focused on describing in a step-by-step way, how a program operates, (statements changing a program’s state)
What is Imperative (or procedural programming)?
$400
Focuses on what the program should accomplish without prescribing how to do it in terms of sequences of actions to be taken
What is Declarative (programming)
$500
programs with ability to treat programs as their data. A program could be designed to read, generate, analyze and/or transform other programs, and even modify itself while running
What is metaprogramming?
$100
What is a Binary Search?
$200
A condition in a recursive module that returns a definite value; it causes the module to stop calling itself.


Toggle navigation
Additional Keyboard Symbols
Browser Keyboard
Language
English
Русский
Contact me
GitHub
Additional custom keyboard symbols extensoin for browsers
Chrome
Firefox
Default Set for Ctrl + Alt + Key
Default Set for Ctrl + Alt + Shift + Key
×
Contact me
I'm Ruslan Sayfullin, developer of "Browser Keyboard" project.
If you found a bug, please file an issue on
Github
Write me if you want to add your language, layout or make any other suggestion
email
<EMAIL_ADDRESS>vk.com
profile


Previous: Examples of Rule of Canonical Representation for Complex Rationals, Up: Complex Computations
Many of the irrational and transcendental functions are multiply defined in the complex domain; for example, there are in general an infinite number of complex values for the logarithm function. In each such case, a principal value must be chosen for the function to return. In general, such values cannot be chosen so as to make the range continuous; lines in the domain called branch cuts must be defined, which in turn define the discontinuities in the range. Common Lisp defines the branch cuts, principal values, and boundary conditions for the complex functions following "Principal Values and Branch Cuts in Complex APL." The branch cut rules that apply to each function are located with the description of that function.
Figure 12–9 lists the identities that are obeyed throughout the applicable portion of the complex domain, even on the branch cuts:
| sin i z = i sinh z | sinh i z = i sin z | arctan i z = i arctanh z |
| cos i z = cosh z | cosh i z = cos z | arcsinh i z = i arcsin z |
| tan i z = i tanh z | arcsin i z = i arcsinh z | arctanh i z = i arctan z |
Figure 12–9: Trigonometric Identities for Complex Domain
The quadrant numbers referred to in the discussions of branch cuts are as illustrated in Figure 12–10.
Imaginary Axis | | II | I | | | ______________________________________ Real Axis | | | III | IV | | | |
Figure 12–9: Quadrant Numbering for Branch Cuts


--- layout: sidebar title: Research ---
Epidemics are one of the greatest public health challenges of the 21st century. We now have more data than ever before that we can leverage in designing dynamic interventions. This has required led to much interest in designing efficient scalable control algorithms that both solve mathematical problems and provide broad parameter-independent policy insights.
On the other hand, the engineering community has had much to learn from epidemiology and mathematical biology in recent years, e.g., epidemic models have been developed that neatly capture problems in cyber-security and data transfer.
My main contributions during my PhD were to provide a synthesis of ways information about heterogeneities within the affected population and the epidemic process could be used in new ways to design better
dynamic control policies that exploited. I used optimal control theory to design optimal containment and spreading policies for heterogeneous biological, malware, and data epidemics, and showed how derived structures can simplify difficult non-convex optimizations and provide easily implementible distributed policies.
Social influence can lead to changes in behavior, choices, and beliefs. Understanding the mechanisms of influence spread is important in effectively targetting interventions, such as public health messages, and designing incentives to aid the spread of desirable actions, such as ethical behavior in a company.
My main contribution has been in delineating the effects of information availability and timing on optimal influence policies.
I have examined optimal seeding strategies under partial network visibility, optimal budget allocation across time for an electoral campaign (including the development of a new centrality measure), the effect of incentives and whistleblowing policies on ethical behavior within a company, and the effect of social comparison and group norms on social group stability.
I also have a methodological interest in deriving optimal control structures that simplify computations in difficult non-convex optimal control problems.
My main contributions here have been in showing that vectors of bang-bang policies with a limited number of switches are optimal for a wide variety of quadratic ODE differential equations (the mean-field regime), proving further timing structures to simplify direct methods, and integrating rigorous descent methods into practical solvers to speed up the computation of switching times.
Efficient monitoring and routing of data in modern networks with no fixed infrastructure is complicated by their distributed nature and energy constraints. However, epidemic-like spreading processes can be used to facilitate communication in uncertain environments, while novel distributed algorithms have been developed to monitor such communications and to aggregate relevant information.
My main contribution in these fields has been to integrate the differing energy availability in these settings across network elements in the design of optimal routing and monitoring policies.
The integration of renewables in the electrical grid at significant scale requires a simultaneous increase in the integration of batteries and electric vehicles to mitigate uncertainty in generation.
I studied how batteries and electric vehicles should be charged and discharged to meet goals and minimize operation costs, making the economic case for storage. My contributions included deriving patent-pending rigorous battery pricing methods for microgrids and developing a synthesis of offline electric vehicle charging methods and identifying major remaining challenges.


I am a self-driven problem solver and innovator who loves to take on additional
responsibilities to challenge myself. I use my cognitive skills to think of efficient ways to do things or
design new processes. Being naturally curious, I always ponder upon how things work and how they can be made
better. I am a quick grasper and have an affinity toward gaining more knowledge. Always excited about and
fascinated with what technology can achieve these days. I always try to do the best I can by putting in
maximum effort.
Currently pursuing graduate studies in Information Technology Management at The University of Texas at Dallas
and am on the path to be a successful Product Owner and Innovator. I aim to push myself out of my comfort zone
by taking up responsibilities that challenge me.
Consulting with i3Mation to develop an automation software that uses Transfer Learning, Image Classification, Flutter, and Firebase
Managing an Agile Project using MS Project, JIRA and Simulation Tools
Designing architecture for an application that provides Optimized Store Navigation to enhance their shopping experience
Designed, developed, and documented a Li-Fi-based Home Automation system and presented a technical paper at an IEEE conference for the same
Developed a classroom management android app that helped students with tests, schedules, etc.
Developed a web-based grocery store for customers to buy products online
Developed a JAVA based hospital management software to manage basic specific hospital-related data
CGPA: 7.04/10
Designed and developed the end to end solution for an Application Builder with JSON-based Rule Engine that reduced load time by 95%
Helped conceptualize the solution for the Application Builder with a JSON-based Rule engine and was responsible for the design and development of the wireframes, middleware, and the database for the Proof of Concept
Ensured the product met or exceeded expectations by excelling in empathizing with clients to gather requirements and feedback and implementing the same within promised deadline
Introduced Firebase to the team that helped make real-time transactions and storage via the cloud
Created a business case that would help in Warehouse Stock Optimization and implemented a Proof of Concept using Machine Learning by researching on the same that would solve over/understocking


| # | {%for day in week%}{{day}} | {%endfor%}
|---|---|
| {{forloop.counter}} | {%for cell in rows%}{{cell}} | {%endfor%}
SUBJECT: {{classroom.class_subject}}
CLASS CODE: {{classroom.classid}}
MEET LINK:{{classroom.c_link}}
{% load static%}
{% include 'classnav.html'%}| # | {%for day in week%}{{day}} | {%endfor%}
|---|---|
| {{forloop.counter}} | {%for cell in rows%}{{cell}} | {%endfor%}
{{post.post}}
{%if post.notes%} {{post.change}} {%endif%}


| Field Summary | ||
|---|---|---|
const Number | DASH_DOT | |
| Represents thin dash-dotted line. | ||
const Number | DASH_DOT_DOT | |
| Represents thin dash-dot-dotted line. | ||
const Number | DASHED | |
| Represents dashed line. | ||
const Number | DOTTED | |
| Represents dotted line. | ||
const Number | DOUBLE | |
| Represents double line. | ||
const Number | HAIR | |
| Represents hair line. | ||
const Number | MEDIUM_DASH_DOT | |
| Represents medium dash-dotted line. | ||
const Number | MEDIUM_DASH_DOT_DOT | |
| Represents medium dash-dot-dotted line. | ||
const Number | MEDIUM_DASHED | |
| Represents medium dashed line. | ||
const Number | NONE | |
| Represents no line. | ||
const Number | MEDIUM | |
| Represents medium line. | ||
const Number | SLANTED_DASH_DOT | |
| Represents slanted medium dash-dotted line. | ||
const Number | THICK | |
| Represents thick line. | ||
const Number | THIN | |
| Represents thin line. |
| Field Detail |
|---|
DASH_DOT | |
const Number DASH_DOT |
DASH_DOT_DOT | |
const Number DASH_DOT_DOT |
DASHED | |
const Number DASHED |
DOTTED | |
const Number DOTTED |
DOUBLE | |
const Number DOUBLE |
HAIR | |
const Number HAIR |
MEDIUM_DASH_DOT | |
const Number MEDIUM_DASH_DOT |
MEDIUM_DASH_DOT_DOT | |
const Number MEDIUM_DASH_DOT_DOT |
MEDIUM_DASHED | |
const Number MEDIUM_DASHED |
NONE | |
const Number NONE |
MEDIUM | |
const Number MEDIUM |
SLANTED_DASH_DOT | |
const Number SLANTED_DASH_DOT |
THICK | |
const Number THICK |
THIN | |
const Number THIN |


Organize your issues with project boards
Did you know you can manage projects in the same place you keep your code? Set up a project board on GitHub to streamline and automate your workflow.
Sort tasks
Add issues and pull requests to your board and prioritize them alongside note cards containing ideas or task lists.
Plan your project
Sort tasks into columns by status. You can label columns with status indicators like "To Do", "In Progress", and "Done".
Automate your workflow
Set up triggering events to save time on project management—we’ll move tasks into the right columns for you.
Track progress
Keep track of everything happening in your project and see exactly what’s changed since the last time you looked.
Share status
Each card has a unique URL, making it easy to share and discuss individual tasks with your team.
 On to the next project!


Thankyou for your visit
Official community for Top Down Citie's Grand Theft Auto Multiplayer servers was created on 2010 for the first server for Gta 3 Lu. TDCS slowly started to get enhancing and get itself on to GTA VC-MP on 2013. When TDCS got much player's and always got heavily loaded on both GTA-Multiplayer's it started improving it self to GTA SA-MP on 2014 which became quite popular. In 2017 TDCS released A Multi-game mode server for GTA IV:MP , Then on 2018 TDCS released a Multi-gamemode with many features for GTA-V:MP and with great success TDCS got acheivement! Now TDCS is one of the most popular gaming-server in GTA-Multiplayer's. Special Thank's to our member's who developed and promoted TDCS.
MEGAMIND
KHATTAKS
MRDK
Check all our server ip's
| GAMES | SERVER | IP/DOMAIN |
|---|---|---|
| GTA 3-LU | [0.1]Top Down City LC-MP Server | wno-host.ddns.net:2301 |
| GTA VC-MP | [0.4]Top Down City VC-MP Server | wno-host.ddns.net:5192 |
| GTA SA-MP | [0.3.7]SA:MP>>*TOP DOWN CITY SERVER*<< | wno-host.ddns.net:7777 |
| CS 1.6 | Top Down City (CS 1.6) | wno-host.ddns.net:27015 |
| GAMES | SERVER | IP/DOMAIN |
|---|---|---|
| GTA SA-MP | [0.3.DL]SA:MP>>*TOP DOWN CITY SERVER*<< | wno-host.ddns.net:7776 |
| GTA Citizen-IV | [Citizen-IV:MP]Top Down City Server | wno-host.ddns.net:30120 |
| GTA Connected | Top Down City :=: GTA-CONNECTED | wno-host.ddns.net:22000 |


[ Home | Overview | Publications | Software/Downloads ] • [ Documentation/Questions | Training Videos and Slides ] • [ People | Acks ]
HPCToolkit is an integrated suite of tools for measurement and analysis of program performance on computers ranging from multicore desktop systems to the nation's largest supercomputers. By using statistical sampling of timers and hardware performance counters, HPCToolkit collects accurate measurements of a program's work, resource consumption, and inefficiency and attributes them to the full calling context in which they occur. HPCToolkit works with multilingual, fully optimized applications that are statically or dynamically linked. Since HPCToolkit uses sampling, measurement has low overhead (1-5%) and scales to large parallel systems. HPCToolkit's presentation tools enable rapid analysis of a program's execution costs, inefficiency, and scaling characteristics both within and across nodes of a parallel system. HPCToolkit supports measurement and analysis of serial codes, threaded codes (e.g. pthreads, OpenMP), MPI, and hybrid (MPI+threads) parallel codes.
The figure below illustrates HPCToolkit's primary components and their relationships.
hpcrun: hpcrun collects accurate and precise calling-context-sensitive performance measurements for unmodified fully optimized applications at very low overhead (1-5%). It uses asynchronous sampling triggered by system timers and performance monitoring unit events to drive collection of call path profiles and optionally traces.
hpcstruct: To associate calling-context-sensitive measurements with source code structure, hpcstruct analyzes fully optimized application binaries and recovers information about their relationship to source code. In particular, hpcstruct relates object code to source code files, procedures, loop nests, and identifies inlined code.
hpcprof: hpcprof overlays call path profiles and traces with program structure computed by hpcstruct and correlates the result with source code. hpcprof/mpi handles thousands of profiles from a parallel execution by performing this correlation in parallel. hpcprof and hpcprof/mpi generate a performance database that can be explored using the hpcviewer user interface.
hpcviewer: hpcviewer is a graphical user interface that interactively presents performance data in three complementary code-centric views (top-down, bottom-up, and flat), as well as a graphical view that enables one to assess performance variability across threads and processes. hpcviewer is designed to facilitate rapid top-down analysis using derived metrics that highlight scalability losses and inefficiency rather than focusing exclusively on program hot spots.
hpcviewer also presents a hierarhical, time-centric view of a program execution. The tool can rapidly render graphical views of trace lines for thousands of processors for an execution tens of minutes long even a laptop. hpcviewer's hierarchical graphical presentation is quite different than that of other tools - it renders execution traces at multiple levels of abstraction by showing activity over time at different call stack depths.
In this project, the goal is to extend HPCToolkit with improved capabilities for measurement and analysis of computation, data movement, communication, and I/O at extreme scale. Planned enhancements include improved support for lightweight measurement of massive node-level parallelism using hardware counters, measuring OS activity, recording measurement data efficiently, analyzing performance data in parallel, and integrating code-centric, time-centric, data-centric, and resource-centric views for analysis.
HPCToolkit is released under the 3-clause BSD license and is available on GitHub/HPCToolkit.
[Page last updated: 2020/12/04]


Welcome back to my 9th tutorial on Machine Learning! I have been kept busy in last weekends, struggling in getting my desktop ready for Deep Learning. You may consider giving that post a look if you are planning to build your own “Monster” too: Building up my own machine for Deep Learning.
Of course I got a lot to tell you about things I’ve been doing with my new desktop. But it will be a little bit selfish of mine and unfair to all of you if I let dust cover my Machine Learning tutorial series. So let’s say, I’m back on track. And today, I will talk about Regularization, a technique to deal with Overfitting problem. You may still remember that I mentioned eariler in previous posts, Overfitting is a big headache in all Machine Learning problems. Beside Cross Validation that I told you before, Regularization is a must-know technique that you will nearly apply in all of your Machine Learning problems. Furthermore, applying Regularization is a default setting in all algorithms provided by scikit-learn library.
And I’m not going to waste any minute of yours. Let’s go straight into the most likely asked question: What is Regularization?
When we hear the word Regularization without anything else related to Machine Learning, we all understand that Regularization is the process of regularizing something, or the process in which something is regularized. The problem is: what is exactly something. In terms of Machine Learning, we talk about learning algorithms or models, and what is actually inside the algorithms or models? That’s the set of parameters. In short, Regularization in Machine Learning is the process of regularizing the parameters.
After knowing that Regularization is actually to regularize our parameters, then you may wonder: Why regularizing the parameters help prevent Overfitting? Let’s consider the graph that I had prepared for this tutorial. A picture is worth a thousand words, right?
As you can see in the graph I have just shown you, we got two functions represented by a green curve and a blue curve respectively. Both curve fit those red points so well that we can consider they both incur zero loss. And if you followed all my previous tutorials, you would be able to point out that the green curve is likely to overfit the data. Yeah, you are totally right. But have you ever wondered why the green curve (or any curve which is similar to it) is overfitting the data?
To understand that in a more mathematical way, let’s consider the two functions that I used to draw the graph above:
The green curve:
The blue curve:
Does it sound similar to you? I once told you about one way to improve the performance of Linear Regression model, that is adding polynomial features. You can refer it here: Underfitting and Overfitting Problems. You knew that by adding more features, we will have a more well learned model which can fit our data far better. But everything has its drawback, if we add so many features, we will be purnished with Overfitting. That was what I told you in the earlier tutorial. Not so hard to recall, right?
If you look at each function’s equation, you will find that the green curve has larger coefficients, and that’s the main caution of Overfitting. As I mentioned before, Overfitting can be interpreted that our model fits the dataset so well, which it seems to memorize the data we showed rather than actually learn from it. Intuitively, having large coefficients can be seen as an evidence of memorizing the data. For example, we got some noises in our training dataset, where the data’s magnitude is far difference than the others, those noises will cause our model to put more weight into the coefficient of higher degree, and what we received is a model that overfits our training data!
Some of you may think, if adding so many features causes Overfitting, than why don’t we just stop adding features when we got an acceptable model? But think about that this way. If your customer or your boss wants a learned model with \(95%\) accuracy, but you can’t achieve that result without adding some more features, which results in overfitting the data. What will you do in the next step?
Or think about it in one more other way. You are facing a problem where you are provided with a large dataset, which each of them contains a great deal of features. You don’t know which features to drop, and even worse if it turns out that every feature is fairly informative, which means that dropping some features will likely ruin the algorithm’s performance. What do you plan to do next?
Therefore, it’s not always a good idea to drop some features just to prevent Overfitting. And as you saw in the example above, it requires further analysis to know whether you can remove some less informative features. So, it’s a good practice that you use all features to build your first model in the beginning. And regularization comes out as a solution. To make it more clear for you, let’s consider our MSE cost function:
I once introduced the MSE cost function before in Logistic Regression tutorial. And as you know, the objective of learning is to minimize that MSE function. It means that our parameters can be updated in anyway, just to lower the MSE value. And as I told you above, the larger our parameters become, the higher chance our Model overfits the data. So the question is: can we not only minimize the cost function, but also restrict the parameters not to become too large? The answer is: we CAN, by adding the regularization term like below:
, where \(\lambda\) is a constant to control the value of regularization term, \(n\) is the number of the features.
Take a look at our new cost function after adding the regularization term. What the regularization term does is to penalize large parameters. Obviously, minimize the cost function consists of minimizing both terms in the right: the MSE term and the regularization term. So each time some parameter is updated to become significantly large, it will increase the value of the cost function by the regularization term, and as a result, it will be penalized and updated to a small value.
Also note that we only compute the regularization term with the weights only, DON’T include the bias in the regularization term! You may ask why? Well, we can re-write our activation function like below (in case of polynomial function):
As you can see, we can think that the bias term goes with the \(X^0\) term, which means that it doesn’t affect to the form of our function, so include the bias term into the regularization term doesn’t make any sense.
With the new added regularization term, obviously we have to make some change to the way we update the parameters too. But it’s not a big deal at all, just take all the partial derivatives and we will achieve the result below very easily:
Next, let’s put things together to see how the parameters are updated after adding regularization term:
That’s it. All we have to change is just adding the factor \((1-\frac{\lambda}{m})\) to the parameter when updating. You can prove the result above yourselves as an assignment. It’s very easy, but I recommend you do it yourselves to become more familiar with those mathematical terms like the Chain Rule or partial derivatives, which you will use a lot in the next tutorials.
Above I have shown you about adding the regularization term in our MSE function, and how to apply regularization in updating parameters, too. But it doesn’t mean that when applying regularization, you always stick to the term I have shown you. In fact, it’s just one among many forms of regularization. It’s just like the way we have many options for the cost function (or you can call it the loss function), we have MSE function, we have log-likelihood cost function, etc. To a particular problem, there will always be more than one approach, and each one will likely work best in a particular set of conditions.
Below I will introduce to you the two which are mostly used in real world projects:
This is actually the one I have shown above. It’s also easy to remember: L2 means degree \(2\) regularization term. Before talking any further, let’s consider the other one first:
Another form of regularization, called the L1 Regularization, looks like above. As you can see, instead of computing mean value of squares of the parameters as L2 Regularization does, what L1 Regularization does is to compute the mean magnitude of the parameters. Also note that in L1 Regularization term, we multiply the sum with a fraction of \(\frac{\lambda}{m}\), not \(\frac{\lambda}{2m}\). Remember that the term \(\frac{\lambda}{2m}\) helps vanish the \(2\) factor in the derivative of polynomial of degree 2. So I hope you won’t be confused which one to use.
Looking at two cost functions above, adding L1 Regularization term or L2 Regularization term have nearly the same effect, that is to penalize large parameters. As a result, we end up with a learned model with all parameters being kept small, so that our model won’t depend on some particular parameters, thus less likely to overfit.
To understand how the two terms differ from each other, let’s see how they affect the parameter update process:
To make it simple, I just computed for one parameter \(\theta_j\). As you can see, when applying regularization, we have the new \((1-\alpha\frac{\lambda}{m})\) factor. What does it affect the update for \(\theta_j\) anyway? After each learning iteration, \(\theta_j\) decreases by an amount of \(\alpha\frac{\lambda}{m}\), which means that L2 Regularization tends to shink it by an amount proportional to \(\theta_j\). The larger \(\theta_j\) becomes, the more it will shrink. Now, what it’s like in the case of L1 Regularization:
When we use L1 Regularization, our parameters shrink in a different way. Because \(sign(\theta_j)\) can only be either \(-1\) or \(1\), \(\theta_j\) now shrinks by a constant amount, and it tends to move toward zero. This makes the update process different from what we saw in L2 Regularization. Therefore, we can easily see that L1 Regularization tends to penalize small parameters more than L2 Regularization does. In short:
Above I have just shown you two mostly applied forms of regularization, and how each of them affect the learning process. The last thing I want to tell you in this post is about the constant \(\lambda\). As I mentioned earlier, \(\lambda\) controls how much we want to regularize our parameters. But what is really behind this? Let’s consider the two scenarios below:
When \(\lambda\) is nearly zero, then the regularization term will become nearly zero. As a result, the cost function mostly depends on the MSE term just like before applying regularization. Or we can say that, when \(\lambda\) is nearly zero, the regularization term won’t have any significant effect on shrinking the parameters. Therefore the model is more likely to overfit.
Conversely, let’s consider the case where \(\lambda\) becomes extremely large. This time we put much weight in the regularization term. And as a result, the parameters will shrink to a very small values. That approach, however, brings a real problem, that is, rather than preventing Overfitting, our parameters now become so small so that it can’t even fit the training data well, or we can say that: applying so much regularization cause our model to underfit the dataset.
Through the two scenarios above, you can see that choosing the right value for \(\lambda\) is not an easy task. But with the right choice of \(\lambda\), the regularization term can have a significant effect on the learning process, which results in a model which both fits the dataset very well, but not likely to overfit.
In today’s post, we have talked about Regularization, an important technique applied in every Machine Learning model in the real world to deal with the Overfitting problem. I hope after this tutorial, you can have a deeper understanding about what actually causes Overfitting, and the right way to deal with that headache. In the next post, I will continue with one last supervised learning algorithm, the one that I should have showed you in this post instead. But I soon realized that with the lack of knowledge about Regularization, it will be pretty hard to fully understand that algorithm. I hope you not blame me for this. So stay updated and I will be right back!


Leland Stanford Purchases Land
1876
Leland Stanford purchases 650 acres of land for a stock farm for his
horses. This farm will become the core of Stanford University campus. Future purchases
by Leland Stanford will add to the property, leading to over 8000 acres total.
University Established
1891
Stanford University is established. The first class has 559 men and women.
There are 15 faculty members the first year, with 49 by the second year. Frederick Law Olmsted,
the architect for New York's Central Park, designs the Stanford campus.
Earthquake Devastates Campus
1906
The great San Francisco Earthquake of 1906 devastates the campus,
permanently changing Stanford's architectural character. An archway over the entrance to the
quad is destroyed. The church is badly damaged and its bell tower is destroyed. See
the Stanford University
and the 1906 Earthquake for photographs and additional information.


SystemAgent
-
System-agent is responsible agent for the utilities of the system in the nugu. This agent is mandatory in SDK.
See moreDeclaration
Swift
public protocol SystemAgentProtocol : CapabilityAgentable
-
The
See moreSystemAgentDelegate
protocol defines methods that action whenSystemAgent
receives a directive.Declaration
Swift
public protocol SystemAgentDelegate : AnyObject
-
<#Description#>
See moreDeclaration
Swift
public enum SystemAgentExceptionCode : Equatable
-
<#Description#>
Declaration
Swift
public enum SystemAgentRevokeReason : String, Codable


Your privacy in one sentence:
Our objective is to ensure the privacy of our users against any type of political opression and repression.
That being said, we are a non-profit, activist-driven initiative, and we do not have the developer resources to sufficiently test and develop our own software for all our current need of data analysis and data handling. This means, that no matter how lean, secure and anonymous firefund.net is, your privacy still rests on the policies of the relating software.
Currently these external vendors are:
- Stripe, as a credit card payment processor,
- Piwik, as a data analysists,
- SendGrid, as a newsletter processor,
- Youtube (Google), as a video content manager,
We will account for these individually and link for further information further down.
We will also account for our policy in terms of communication internally and externally.
We will finally inform about certain general pitfalls for your data privacy, when using our site, and how to avoid them.
Feedback and contributions
FIREFUND (www.firefund.net) and (in the future) our Tor site (www.firefundezoptrah.onion) are essentially open-source community-driven ressources - and any feedback, questions, proposals are much appreciated.
But in reality, the most valuable method is direct action - so if you wish to change aspects of our privacy policies and data handling systems, then please get in contact by<EMAIL_ADDRESS>or reach out to our github organisation:
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Cookies are small files saved to the user's computer hard drive that track, save and store information about the user's interactions and usage of the website. If you wish to deny the use and saving of cookies from this website, you should take necessary steps within your web browsers security settings to block all cookies from this website.
However, blocking the use of cookies from this website, may give some problems in paying with credit cards.
1. Stripe
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If you choose to donate to a campaign with your credit card, you will use the Stripe application. Stripe is a payment provider, that handles the data from your input when you donate, verifies the payment, withdraws the donation from your bankaccount and transfers the donation to FIREFUND and/or directly to the receiving party.
 If your donation is illegal, either because of the money being donated is illegal, or because the initiative you are supporting are illegal in any way, then there is no way for FIREFUND to ensure you of your safety against legal repression.
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Currently, there is no active implementation of Piwik on FIREFUND.net. This privacy policy will be updated before we start using Piwik.
The main reason we are using Piwik as a provider of data from the usage of the website, is that with Piwik, we as a platform keep control of the data and we will never sell, give or share it with others.
Read more on the Piwik privacy on their website
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If you choose to sign up for our newsletter, or if you input your email in any other field on the website, it will most likely end up in our database hosted by SendGrid. When we use SendGrid to distribute newsletters, 'call to actions', donation confirmations or other kinds of emails, we will (via. SendGrid) include various types of tracking facilities to gather data on the usage of our newsletters. This might include data on; the opening of emails, forwarding of emails, clicking on the links within the emails, times, dates and frequency of activity and interaction with the emails in general.
As with the rest of the external vendors, we will never give out access to these databases of data to any third parties, and we will do what we can to anonymize the data when it is needed. You will always have the option of unsubscribing the newsletter, via. a link in the bottom of all newsletters.
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External links and social media
We can only take responsability for your security and anonymity while visiting this site. We will have links to external websites posted frequently and we cannot assure the security meassures taken on these sites. Same goes for various links and icons directing you to social media platforms. We use the infrastructure of different social media platforms to host discussions, create community, reach out to a broader audience and to mobilise. When engaging in communication with us or other people in relation to FIREFUND, keep in mind that our privacy policy does not apply there.
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In order to reduce a boilerplate necessary to start the adventure with any database, Nest comes with the ready to use @nestjs/typeorm
package.
We selected TypeORM because it's definitely the most mature Object Relational Mapper (ORM) available so far.
Since it's written in TypeScript, it works pretty well with the Nest framework.
Firstly, we need to install all of the required dependencies:
$ npm install --save @nestjs/typeorm typeorm mysql
Notice In this chapter we'll use a MySQL database, but TypeORM provides a support for a lot of different databases such as PostgreSQL, SQLite, and even MongoDB (NoSQL).
Once the installation process is completed, we can import the TypeOrmModule
into the root ApplicationModule
.
{{ importTypeOrm }}
The forRoot()
method accepts the same configuration object as createConnection()
from the TypeORM package.
Futhermore, instead of passing anything to the forRoot()
, we can create an ormconfig.json
file in the project root directory.
{{ ormconfig }}
Then, we can simply leave the parenthesis empty:
{{ 'app.module' | extension: importTypeOrmEmptyT.isJsActive }}{{ importTypeOrmEmpty }}
Afterward, the Connection
and EntityManager
will be available to inject across entire project (without importing any module elsewhere), for example, in this way:
{{ importConnectionInstance }}
{{ importConnectionInstanceJs }}
The TypeORM supports the repository design pattern, so each entity has its own Repository. These repositories can be obtained from the database connection.
Firstly, we need at least one entity. We're gonna reuse the Photo
entity from the offical documentation.
{{ photoEntity }}
The Photo
entity belongs to the photo
directory.
This directory represents the PhotoModule
. It's your decision where you gonna keep your model files. From our point of view, the best way's to hold them nearly their domain, in the corresponding module directory.
Let's have a look at the PhotoModule
:
{{ photoModule }}
This module uses forFeature()
method to define which repositories shall be registered in the current scope.
Thanks to that we can inject the PhotoRepository
to the PhotoService
using the @InjectRepository()
decorator:
{{ photoService }}
{{ photoServiceJs }}
Notice Don't forget to import thePhotoModule
into the rootApplicationModule
.
Some of your projects may require multiple database connections. Fortunately, this can also be achieved with this module. To work with multiple connections, the first thing to do is to create those connections. In this case, the connection naming becomes mandatory.
Say you have a Person
entity and a an Album
entity, each stored in their own database.
{{ multipleConnections }}
Notice If you don't set anyname
for a connection, its name is set todefault
. Please note that you shouldn't have multiple connections without a name, or with the same name, otherwise they simply get overridden.
At this point, you have each of your Photo
, Person
and Album
entities registered in their own connection.
With this setup, you have to tell the TypeOrmModule.forFeature()
function and the @InjectRepository()
decorator which connection should be used.
If you do not pass any connection name, the default
connection is used.
{{ forFeatureWithConnection }}
You can also inject the Connection
or EntityManager
for a given connection:
{{ injectConnectionAndEntityManager }}
When it comes to unit test our application, we usually want to avoid any database connection, making our test suits independent and their execution process quick as possible.
But our classes might depend on repositories that are pulled from the connection instance. What's then?
The solution is to create fake repositories. In order to achieve that, we should set up custom providers.
In fact, each registered repository is represented by EntityNameRepository
token, where EntityName
is a name of your entity class.
The @nestjs/typeorm
package exposes getRepositoryToken()
function that returns prepared token based on a given entity.
{{ mockRepository }}
Now a hardcoded mockRepository
will be used as a PhotoRepository
.
Whenever any provider asks for PhotoRepository
using an @InjectRepository()
decorator, Nest will use a registered mockRepository
object.
A working example is available here.


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I will create a storm topology to read data via tweets "twitter API" for java. This topology will look at the content of tweets to found the key words related to the disciplines of current Olympic Games "Rio 2016". In this example I worked with the HDP (Hortonworks Data Platform) 2.4 Sandbox.
Step 1: Start Storm Service by Ambari
Go to ambari page and start Storm Service in the cluster.
Step 2: Twitter application
The Twitter API use OAuth broadcast to authorize requests. To use OAuth, the first step is to create a new application on the Twitter Developer site. To create the ID and the secrets of a Twitter application:
Step 3: Topology Creation
I decided to create a simple topology. This topology is going to read data via tweets "twitter API" for java and in a second time it will found in the content of tweets the key words related to the disciplines of current Olympic Games "Rio 2016". Once treatment has recovered the searched keywords (disciplines in the Olympics) it will count the number of occurrences for each word to generate a ranking of the most popular sports I have used in my topology one Spout and 4 Bolts. Here is a simplified diagram of the topology :
Spout: "Twitter Spout" Reads Twitter's sample feed using the twitter4j library.
Bolt: "WordSplitterBolt» Receives tweets and emits its words over a certain length.
Bolt: "FilterWords Bolt" Bolt filter a predefined set of words about the disciplines of Olympic Games Rio 2016.
Bolt: " OlympicWordsCounterBolt" Keeps stats on word count, calculates and logs top words about the Olympics disciplines every X second to stdout and top list every Y seconds.
Bolt: "HdfsBoltWriter" Write the ranking of disciplines in Hdfs: /path/in/hdfs
Here is an excerpt of the Java code.
Topology.java
TwitterSpout.java
FilterWordsBolt.java
WordSplitterBolt.java
WordCounterBolt.java
Step 3: Launch the topology with storm in the cluster.
I used the command “storm jar storm-twitter-olympics-ranking-0.1-shaded.jar com.moisestrelles.storm.Topology” to launch my topology.
Tweets are comming:
Step 4: Output verification
I verified the output result in the HDFS folder:
Let’s see the output final files, this files contains the liste of disciplines and the number of times this disciplines were mentioned in the twitts:


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恭喜! {{ course.class_title }} {{ course.class_code }}（课号{{ course.class_number }}）已被您收入囊中！
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Hello, {{ user.username }},
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Home
Lectures
Linux command
VI Editor
VMD
Project1
Sequence Alignment
Homology Modeling
Get TOP & CRD
Dynamics Simulation
MD Analysis
Project2
Assessment


User guide¶
Overview¶
ipyreact is a complementary project to ipywidgets aiming to provide visual widgets built using the React framework. The main goal is to provide UI components using Material UI. Material UI allows its user to create great looking, easy to use applications implementing UI standards. Our aim is to provide a Material UI implementation for the Jupyter world, in order to ease the dashboard building process for Jupyter Notebook.


The last two colors get used for the links and text respectively, but you can use them for your circles too.
NB. There's no error checking. Badly formed JSON or mismatched ids will make the app fail silently.
The link "type" property allows you to specify whether the link is "double" (delegate plus leader) or single. The "source" and "target" properties need to match the circle "id" values in the top field.
Works as intended in Chrome 70.0.3538.102. No guarantees about any other browser. Uses the D3 library. I hacked this together because I couldn't find a tool to speed up making/modifying the circle diagrams for sociocratic organisations. My email: info at redshift media dot com.